给出一个numpy实现多层前馈神经网络的代码

import numpy as np

class NeuralNetwork:
    def __init__(self, layers, neurons):
        """
        layers: list, 每层神经元个数，例如[2,3,1]表示3层网络，第1层2个神经元，第2层3个神经元，第3层1个神经元
        """
        self.layers = layers
        self.neurons = neurons
        self.weights = []
        self.biases = []
        
        # 初始化权重和偏置
        for i in range(len(layers) - 1):
            self.weights.append(np.random.randn(layers[i], layers[i+1]))
            self.biases.append(np.random.randn(layers[i+1]))
            
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def forward(self, input_data):
        """
        input_data: array-like, 输入数据
        """
        activations = [input_data]
        for i in range(len(self.layers) - 1):
            z = np.dot(activations[i], self.weights[i]) + self.biases[i]
            a = self.sigmoid(z)
            activations.append(a)
        return activations
    
# 创建一个3层网络，第1层2个神经元，第2层3个神经元，第3层1个神经元
nn = NeuralNetwork([2, 3, 1], 1)

# 随机生成一组输入数据
input_data = np.random.randn(2)
# 计算网络输出
output = nn.forward(input_data)